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10 Newer Entries Showing entries 31 to 40 of 162 10 Older Entries

Displaying posts with tag: Tokutek (reset)

MongoDB Transactions? Yes
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People claim that MongoDB is not transactional. It actually is, and that’s a good thing.

In MongoDB 2.2, individual operations are Atomic. By having per database locks control reads and writes to collections, write operations on collections are Consistent and Isolated. With journaling on, operations may be made Durable. Put these properties together, and you have basic ACID properties for transactions.

The shortcoming with MongoDB’s implementation is that these semantics apply to individual write operations, such as an individual insert or individual update. If a MongoDB statement updates 10 rows, and something goes wrong with the fifth row, then the statement

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TokuDB Fast Update Benchmark
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Last month my colleague Rich Prohaska covered the technical details of our “Fast Update” feature which we added to TokuDB in version 6.6.  The message based architecture of Fractal Tree Indexes allows us to defer certain operations while still maintaining the semantics that MySQL users require.

In the case of Fast Updates, TokuDB is avoiding the read-before-write requirement that the existing MySQL update statement imposes on storage engines.  We can simply inject an update message into the Fractal Tree Index, and apply that message at a later time.  The message is dynamically applied if a user selects that specific

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Wanted: Evaluators to Try MongoDB with Fractal Tree Indexing
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We recently resumed our discussion around bringing Fractal Tree indexes to MongoDB.  This effort includes Tokutek’s interview with Jeff Kelly at Strata as well as my two recent tech blogs which describe the compression achieved on a generic MongoDB data set and performance improvements we measured using on our implementation of Sysbench for MongoDB.  I have a full line-up of benchmarks and blogs planned for the next few months, as our project continues.  Many of these

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Sysbench Benchmark for MongoDB
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As we continue to test our Fractal Tree Indexing with MongoDB, I’ve been updating my benchmark infrastructure so I can compare performance, correctness, and resource utilization.  Sysbench has long been a standard for testing MySQL performance, so I created a version that is compatible with MongoDB.  You can grab my current version of Sysbench for MongoDB here.

So what exactly is Sysbench?  According to the Sysbench homepage, “Sysbench is a modular, cross-platform and multi-threaded benchmark tool for evaluating OS [Operating System] parameters that are important for a system running a database under intensive load.”

  • Sysbench schema
    • 16 copies of the same collection,

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The Last Mile for Big Data – Strata Overview with Jeff Kelly of Wikibon (Part 2)
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During the second half of our CUBE discussion with Wikibon analyst Jeff Kelly at this year’s Strata Conference in Santa Clara, we talked about the tipping point for Big Data. Strata veterans could see at a glance that this year’s conference was markedly different. No longer the exclusive domain of geeks and database administrators, this year’s Strata featured some of the biggest enterprise vendors around. With heavy weight enterprise players Intel and EMC Greenplum announcing their own Hadoop distributions, big data is clearly going mainstream. Now that we know how to capture, store, access and analyze big data, what’s the next step? Listen in to hear my conversation with Jeff Kelly about taking big data

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MySQL and MongoDB – Strata Discussion with Jeff Kelly of Wikibon (Part 1)
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We had the opportunity to do a CUBE interview with Wikibon analyst Jeff Kelly at last week’s Strata Conference in Santa Clara. In the first part of our conversation, we discuss how our success in integrating Tokutek’s Fractal Tree® technology into MySQL has led us to another popular database, MongoDB. We explain the results of our recent benchmarking tests with MongoDB, which indicate that adding indexing can also improve performance for this popular NoSQL database with faster insertion rates, lower query latency and

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MongoDB + Fractal Tree Indexes = High Compression
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One doesn’t have to look far to see that there is strong interest in MongoDB compression. MongoDB has an open ticket from 2009 titled “Option to Store Data Compressed” with Fix Version/s planned but not scheduled. The ticket has a lot of comments, mostly from MongoDB users explaining their use-cases for the feature. For example, Khalid Salomão notes that “Compression would be very good to reduce storage cost and improve IO performance” and Andy notes that “SSD is getting more and more common for

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NoSQL is Great, But You Still Need Indexes
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I’ve said it before, and, as is the nature of these things, I’ll almost certainly say it again: your database performance is only as good as your indexes.

That’s the grand thesis, so what does that mean? In any DB system — SQL, NoSQL, NewSQL, PostSQL, … — data gets ingested and organized. And the system answers queries. The pain point for most users is around the speed to answer queries. And the query speed (both latency and throughput, to be exact) depend on how the data is organized. In short: Good Indexes, Fast Queries; Poor Indexes, Slow Queries.

But building indexes is hard work, or at least it has been for the last several decades, because almost all indexing is done with B-trees. That’s true of

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Concurrency Improvements in TokuDB v6.6 (Part 1)
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With TokuDB v6.6 out now, I’m excited to present one of my favorite enhancements: concurrency within a single index. Previously, while there could be many SQL transactions in-flight at any given moment, operations inside a single index were fairly serialized. We’ve been working on concurrency for a few versions, and things have been getting a lot better over time. Today I’ll talk about what to expect from v6.6. Next time, we’ll see why.

Summary of Results

Running multiple iiBench clients on a single MySQL instance, we see a big improvement in the cumulative insertion speed at all concurrency levels. We see a gain of 33.9% in single-threaded performance and 51.8% at

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Tracking 5.3 Billion Mutations: Using MySQL for Genomic Big Data
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University of Montreal Tracks Genomic Data With Tokutek’s TokuDB.

Faster insertion rates, improved scalability and agility support lab’s fast growing research database as it grows from 100s of GBs to 1 TB and beyond.

Issue addressed: MySQL database used for genomic research must be able to quickly ingest huge amounts of incoming data – hundreds of thousands of records every day. It also must be able to retrieve data quickly in response to a diverse set of research requests.

Enabling the Hunt for New Cures for Diseases by Seamlessly Processing Billions of Mutations  [Read more...]

10 Newer Entries Showing entries 31 to 40 of 162 10 Older Entries

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